The vanishing point (VP) is particularly important road information, which provides an important judgment criterion for the autonomous driving system. Existing vanishing point detection methods lack speed and accuracy when dealing with real road environments. This paper proposes a fast vanishing point detection method based on row space features. By analyzing the row space features, clustering candidates for similar vanishing points in the row space are performed, and then motion vectors are screened for the vanishing points in the candidate lines. The experimental results show that the average error of the normalized Euclidean distance is 0.0023716 in driving scenes under various lighting conditions. The unique candidate row space greatly reduces the amount of calculation, making the real-time FPS up to 86. It can be concluded that the fast vanishing point detection proposed in this paper would be suitable for high-speed driving scenarios.
Structural health monitoring (SHM) has been widely applied in the field of Mechanical and Civil Engineering in recent years. It is very hard to detect damage, however, using the measured data directly from the remote cloud platform of on-site structure, owing to changing environmental conditions. At the same time, outlier data from the remote cloud platform often occurs due to the harsh environmental conditions, interferences in the wireless medium, and the usage of low-quality sensors, which can greatly reduce the accuracy of structural health monitoring. In this paper, a novel temperature compensation method based on a long-short term memory (LSTM) network and the particle filter (PF) is proposed to separate the temperature effect from long-term structural health monitoring data. This method takes LSTMs as the state equation of PF, which solves the problem whereby PF cannot accurately derive the state equation for complex structures. A feedback model using the probability distribution generated by PF is developed to filter the observed value, thus measurement outliers can be successfully reduced. A numerical simulation and the measured deflection data from an SHM system are utilized to verify the proposed method. Results from the numerical simulation show that the LSTM-PF method can satisfactorily compensate for the temperature effect even when the nonlinear temperature effect is considered. Moreover, outputs from the SHM system of a large-scale suspension bridge indicate the temperature effect can be compensated and outliers can be appropriately reduced at the same time using the measured deflection data.
Pixel segmentation is one of the most commonly used deep learning methods for modern lane line detection. Although deep segmentation outperforms traditional methods, there are two main problems: slow speed and limited receptive field. In response to these problems, this paper proposes a lightweight lane line detection algorithm based on learnable cluster segmentation and self‐attention mechanism, which has extremely fast speed and the ability to adapt to real scenes. The lane detection process is considered as clustering under row segmentation. The data is processed through row segmentation and fed into a self‐attention mechanism. In addition to the benchmark dataset for lane detection, the algorithm was ported to real vehicles for real‐time road testing. Two tests show that our method performs very well on TuSimple, with an accuracy of 97.15%, an F1 score of 73.5 on CULane, and a speed of 142.7 frames per second (FPS), which solves the problem of slow cluster segmentation, while improving the accuracy of row segmentation. In the new scenario, the method has a misjudgment rate of only 6.7% for lane line points, which is suitable for the high standard requirements of autonomous driving.
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